Abstract
The learning of programming is traditionally challenging for students. However, this is also one of the most fundamental skills for any computer scientist, and is becoming an important skill in other areas of knowledge. In this paper we analyze the use of game-elements in a challenging long-term programming task, with students of the 3rd year of a Informatics Engineering degree. We conducted a quantitative study using the AMS scale to assess students' motivation. Results show that with the use of game-elements, students are both intrinsically and extrinsically motivated, and that they consider learning/working fun, which contributes positively to their academic performance. 2012 ACM Subject Classification Human-centered computing!Collaborative and social computing theory, concepts and paradigms.

Abstract

Abstract
Nowadays, the world is getting increasingly competitive and the quality and the amount of the work presented are one of the decisive factors when choosing an employee. It is no longer necessary to only perform but, to achieve a product with quality, on time, at the lowest possible cost and with the minimum resources. For this reason, the employee must have a high score of attention when performing a task, and the factors that influence attention negatively must be reduced. This is true in many different domains, from the workplace to the classroom. In this paper, we present a nonintrusive smart environment for monitoring people's attention when working in teams. The presented system provides real time information about each individual and information about the team. It can be very useful for team managers to identify potentially distracting events or individuals because when the attention of an individual is not at its best when performing the proposed task, her/his performance will be negatively affected, with consequences for the individual and for the organization.

Abstract
In the current world, performance is one of the most important issues concerning work and competition. Performance is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms as the level of the learner's attention affects learning results. When students are doing learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage. The goal of this research is to propose a system that measures the level of attentiveness in real scenarios, and detects patterns of behavior associated to different attention levels among different students. This system measures attention and uses this information for training a decision support system that shows the level of attention of a group of students in real time.

Abstract
Traditionally, the Teacher-Student relationship is a close one. The student spends several hours of a day in the presence of the teacher and can talk, express doubts and pose questions. These doubts, or the general feeling towards the object of learning, are not only expressed explicitly but also implicitly. Indeed, the teacher is constantly, even if in an unconscious way, reading the state of the student in search for sings of doubt, frustration, stress or fatigue. This information is then used by the teacher to adapt their methods or to personalize their approach in function of each student. These aspects, intuitively central in education, become less efficient when learning takes place in a Virtual Environment. Indeed, the growth of online courses, in which the student and the teacher often never even meet, make learning more difficult for a number of reasons. In this paper we analyse these reasons and put forward an approach for inferring the student's state that aims to minimize the effects of the absence of the teacher.

Abstract
In traditional learning, teachers can easily get an insight into how their students work and learn and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. With the enormous growing of e-learning platforms, as complementary or even primary tool to support learning in organizations, monitoring students' success factors becomes a crucial issue. In this paper we focus on the importance of stress in the learning process. Stress detection in an E-learning environment is an important and crucial factor to success. Estimating, in a non-invasive way, the students' levels of stress, and taking measures to deal with it, is then the goal of this paper. Moodle, by being one of the most used e-learning platforms is used to test the log tool referred in this work. © Springer International Publishing Switzerland 2013.